Data centers or large clusters of servers have become increasingly employed in universities, enterprises and consumer settings to run a variety of applications such as web services, instant messaging, gaming, data analysis, scientific computing and many others. Data centers typically comprise many thousands of servers arranged hierarchically, typically with racks containing 10-40 servers each, linked by a Data Center Network (DCN).
FIG. 1 is a schematic diagram of a traditional data center network. This shows a hierarchical architecture in which the bulk of the traffic is between the servers and the outside network, so-called “north-south” traffic. The data center (1) comprises a link to external networks (2), servers (6) and a switching hierarchy (7) comprising core routers (3), access routers (4) and switches (5).
With the advent of cloud computing, the data patterns in such networks have changed. In particular, traffic flows between workloads are no longer contained in a single physical server. As a consequence, each server handles multiple workloads. Thus, there is a continuous need to exchange data among servers inside a data center. Instead of the predominance “north-south” traffic, the bulk of the traffic is now “east-west”, between servers. This change has resulted in an evolution in the design of the topology and operation of data centers.
Instead of the hierarchical architecture, data center networks have evolved towards a “flat” topology. FIG. 2 is a schematic diagram of such a “flat network”, comprising a cross point switch (8), to improve interconnectivity between servers (6).
Although very much more suitable for cloud computing applications and their characteristic data flows, the flat architecture is not entirely satisfactory. The problem lies in large data flows, known as “elephant flows”, which typically originate from server back-up or virtual machine migration. Elephant flows are comparatively rare, but when they are present, they can dominate a data center network at the expense of smaller so-called “mice flows”. This can have a highly detrimental effect on the quality of service of mice flows, which are typically delay sensitive. “Mice” flows may be characterized as being latency sensitive short-lived flows, typical of active interaction among machines and real time processing. “Elephant” flows may be characterized as bandwidth intensive flows, for which throughput is more important than latency. Elephant flows may further be considered as having a relatively large size, e.g. larger than a threshold. Elephant flows tend to fill network buffers end-to-end and to introduce big delays to the latency-sensitive mice flows which share the same buffers. The result is a performance degradation of the internal network.
One solution to this problem is the use of “packet offload”, wherein a separate network is provided for elephant flows. The idea of optimizing infrastructure through offloading is not new. For example, in legacy networks, big bulks of Synchronous Digital Hierarchy (SDH) circuits were “offloaded” on DWDM point-to-point trunks.
FIG. 3 is a schematic diagram of an optical offload network for a data center network according to the prior art. In addition to the usual electrical switching arrangements (3,5), there is an optical network (10). Each rack of servers has a top of rack (ToR) switch (9), each of which is connected to the optical network (10). The optical network comprises an optical cross-connect (11) in the form of a Micro-electrical mechanical switch (MEMS). Although effective in providing an optical offload, such a network is complex to implement.